To analyze the research status and development prospects of hydrogels in the field of bone regeneration based on bibliometric methods. In the Web of Science Core Collection database, a total of 1382 representative articles related to the application of hydrogels in bone regeneration were selected as of the end of 2023. Utilizing the tools VOSviewer and CiteSpace, an in-depth bibliometric visualization analysis was conducted to reveal the countries, authors, core literature, and journals that are highly productive in this field. Additionally, the research trends and development prospects within this area were analyzed. This study uses VOSviewer and CiteSpace to analyze 1382 highly cited articles on bone regeneration and materials science, revealing key trends and the evolution of the field. The United States, China, South Korea, Germany, the UK, and Japan are the leading countries in scientific output, with strong collaboration networks. Major research institutions, particularly in China and Japan, focus on hydrogels for bone regeneration. Top journals include International Journal of Biological Macromolecules and Acta Biomaterialia. Key authors include Timothy E. L. Douglas, known for developing mineralization-enhanced hydrogels, and Robert E. Guldberg, focused on growth factor delivery systems. The field has shifted from simple filling materials to multifunctional composites, with advancements in gene editing, signaling molecules, and bone regeneration drugs. Future trends include smart hydrogels, 3D printing, bioactive factor integration, cell engineering, and optimizing biodegradability and biocompatibility. Multifunctional composite hydrogels are expected to become the go-to materials for clinical bone repair. Advancements in technology have transformed hydrogels from simple fillers to multifunctional composites, playing a crucial role in bone regeneration. Future trends include smart hydrogels, 3D printing, bioactive factor integration, cell engineering, and optimized biodegradability and biocompatibility. These developments indicate hydrogels will become the preferred material for clinical bone defect repair, significantly enhancing bone tissue repair and regeneration.

Objective: Radiographic recurrence after hallux valgus surgery is an important issue affecting long-term imaging outcomes. Previous studies have suggested that preoperative deformity, postoperative residual HVA/IMA, and sesamoid reduction are associated with recurrence, but nonlinear relationships may exist among multiple parameters. The aim of this study was to establish and temporally validate a machine learning prediction model based on conventional perioperative clinical and imaging parameters and to provide post-hoc model interpretation using SHAP. Methods: 597 patients undergoing hallux valgus surgery in the Department of Trauma, First Affiliated Hospital of Xinjiang Medical University from 2021 to 2025 were retrospectively enrolled. Radiographic recurrence was defined as HVA >20° on the fixed 12-month postoperative weight-bearing anteroposterior radiograph. Patients operated from 2021 to 2023 formed the development cohort ( n =389), and patients operated from 2024 to 2025 formed the independent temporal-validation cohort ( n =208). Eighteen candidate predictors were screened in the development cohort using collinearity assessment, LASSO, RFE, and permutation importance. A unified eight-feature set was then locked and used for comparison of LR, RF, XGBoost, LightGBM, and SVM using 5-fold outer/3-fold inner nested cross-validation conditional on the fixed feature set. Models were evaluated using ROC-AUC, PR-AUC, Brier score, calibration, and DCA, with post-hoc SHAP-based interpretation of the final SVM. Results: A total of 109 patients met the 1-year radiographic recurrence criterion (18.3%). Eight variables were retained: age, preoperative HVA, preoperative IMA, preoperative DMAA, 6-week postoperative HVA, 6-week postoperative IMA, 6-week postoperative DMAA, and 6-week postoperative sesamoid grade. The development-cohort ROC-AUCs of LR and SVM were 0.908 and 0.906, respectively, with corresponding PR-AUCs of 0.766 and 0.757. SVM was retained as the final model on the basis of its lower Brier score and overall performance profile, without claiming statistical superiority over LR. In the temporal-validation cohort, SVM ROC-AUC was 0.944 (95% CI 0.908–0.973), PR-AUC 0.805, Brier score 0.071, sensitivity 0.892, and specificity 0.842. SHAP showed the largest model contributions for 6-week IMA, HVA, and sesamoid grade. Conclusion: The SVM model with post-hoc SHAP-based interpretation showed promising performance for prediction of fixed 1-year radiographic recurrence using routine clinical and perioperative imaging parameters. Residual IMA, HVA, and sesamoid grade at 6 weeks were leading predictive signals within the model rather than causal determinants of recurrence. The model may support early risk stratification and enhanced follow-up, but multicenter external validation, reproducibility assessment, and prospective impact studies remain necessary before clinical implementation.